import sys
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()
results_folder = 'mmvec_major_taxa_scrambled_1'
results_base_name = 'latent_dim_3_input_prior_1.00_output_prior_1.00_beta1_0.90_beta2_0.95'
table = pd.read_table(results_folder + '/' + results_base_name + '_ranks.txt', index_col=0)
table.head()
| Propionibacteriaceae | Staphylococcus caprae or capitis | Staphylococcus epidermidis | Staphylococcus hominis | Other Staphylococci | Polyomavirus HPyV6 | Polyomavirus HPyV7 | Merkel Cell Polyomavirus | Malasseziaceae | Corynebacteriaceae | Micrococcaceae | Other families | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| featureid | ||||||||||||
| X940001 | 0.030385 | 0.195576 | 0.445758 | 0.098116 | -0.038128 | 0.110024 | 0.096592 | 0.224846 | 0.107593 | 0.190436 | 0.212981 | 0.253598 |
| X940002 | -0.030952 | -0.049524 | -0.063730 | -0.052636 | -0.025059 | -0.050213 | 0.000735 | -0.010614 | -0.028183 | -0.048677 | -0.051594 | -0.026199 |
| X940005 | -0.068787 | 0.051849 | -0.149584 | 0.244480 | 0.030714 | 0.199537 | 0.081876 | -0.019941 | 0.038696 | 0.040158 | 0.083170 | 0.004477 |
| X940007 | 0.414870 | 0.474137 | 0.310297 | 0.606715 | 0.428299 | 0.590012 | 0.442074 | 0.366284 | 0.487615 | 0.444503 | 0.496156 | 0.417681 |
| X940010 | 0.214173 | 0.559161 | 0.656579 | 0.715679 | 0.409365 | 0.613173 | 0.255437 | 0.322402 | 0.259179 | 0.617511 | 0.598442 | 0.375538 |
table['Selected'] = np.isin(table.index,
['X940203', 'X940589', 'X940625', 'X940925', 'X940936', 'X942191',
'X942237', 'X950023', 'X950028', 'X950056', 'X950157', 'X950173',
'X950193', 'X950225', 'X950228', 'X950233', 'X950254', 'X950396',
'X950485', 'X950584', 'X950661', 'X950999', 'X960035', 'X960242',
'X960306', 'X960421', 'X960463', 'X960465', 'X960712', 'X960726',
'X960934', 'X961553', 'X961686', 'X970018', 'X970091', 'X970092',
'X970232', 'X970283', 'X970327', 'X970342', 'X970633', 'X970680']
)
table.sort_values('Selected', inplace=True)
sns.relplot(
table,
y='Propionibacteriaceae', x='Staphylococcus epidermidis', hue='Selected'
)
<seaborn.axisgrid.FacetGrid at 0x7fc084ca14d0>
sns.pairplot(table, hue='Selected')
<seaborn.axisgrid.PairGrid at 0x7fc0848733d0>
for i in table.columns[:-1]:
sns.displot(table, x=i, hue='Selected', multiple='stack')